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ForeachPointwiseOp.cu
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ForeachPointwiseOp.cu
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/Dispatch.h>
#include <ATen/NumericUtils.h>
#include <ATen/native/ForeachUtils.h>
#include <ATen/native/cuda/ForeachFunctors.cuh>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/_foreach_add_native.h>
#include <ATen/ops/_foreach_addcdiv_native.h>
#include <ATen/ops/_foreach_addcmul_native.h>
#include <ATen/ops/_foreach_div_native.h>
#include <ATen/ops/_foreach_maximum_native.h>
#include <ATen/ops/_foreach_minimum_native.h>
#include <ATen/ops/_foreach_mul_native.h>
#include <ATen/ops/_foreach_sub_native.h>
#include <ATen/ops/empty_like_native.h>
#endif
namespace at::native {
template <template <class> class Op>
std::vector<Tensor> foreach_pointwise_op(
TensorList input,
TensorList tensors1,
TensorList tensors2,
const Scalar& scalar) {
std::vector<std::vector<at::Tensor>> tensor_lists;
std::vector<at::Tensor> vec_res;
vec_res.reserve(input.size());
for (const auto& t : input) {
vec_res.emplace_back(at::native::empty_like(t));
}
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
tensor_lists.emplace_back(std::move(vec_res));
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
kHalf,
kBFloat16,
input[0].scalar_type(),
"foreach_pointwise_op_cuda",
[&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<4>(
tensor_lists,
PointwiseOpScalarFunctor<
scalar_t,
/* depth */ 4,
/* r_args_depth */ 3,
/* res_arg_index */ 3>(),
Op<opmath_t>(),
scalar.to<opmath_t>());
});
return tensor_lists[3];
}
template <template <class> class Op>
void foreach_pointwise_op_(
TensorList input,
TensorList tensors1,
TensorList tensors2,
const Scalar& scalar) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
kHalf,
kBFloat16,
input[0].scalar_type(),
"foreach_pointwise_op__cuda",
[&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<3>(
tensor_lists,
PointwiseOpScalarFunctor<
scalar_t,
/* depth */ 3,
/* r_args_depth */ 3,
/* res_arg_index */ 0>(),
Op<opmath_t>(),
scalar.to<opmath_t>());
});
increment_version(input);
}
template <template <class> class Op>
void foreach_pointwise_op_(
TensorList input,
TensorList tensors1,
TensorList tensors2,
at::ArrayRef<Scalar> scalars) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.reserve(3);
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
kHalf,
kBFloat16,
input[0].scalar_type(),
"foreach_pointwise_op__cuda",
[&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<3, opmath_t>(
tensor_lists,
scalars,
PointwiseOpScalarListFunctor<
scalar_t,
/* depth */ 3,
/* r_args_depth */ 3,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
increment_version(input);
}
template <template <class> class Op>
std::vector<Tensor> foreach_pointwise_op(
TensorList input,
TensorList tensors1,
TensorList tensors2,
at::ArrayRef<Scalar> scalars) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.reserve(4);
std::vector<at::Tensor> vec_res;
vec_res.reserve(input.size());
for (const auto& t : input) {
vec_res.emplace_back(at::native::empty_like(t));
}
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
tensor_lists.emplace_back(std::move(vec_res));
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
kHalf,
kBFloat16,
input[0].scalar_type(),
"foreach_pointwise_op_cuda",
[&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<4, opmath_t>(
tensor_lists,
scalars,
PointwiseOpScalarListFunctor<
scalar_t,
/* depth */ 4,
/* r_args_depth */ 3,
/* res_arg_index */ 3>(),
Op<opmath_t>());
});
return tensor_lists[3];
}
#define FOREACH_POINTWISE_OP_SCALAR(NAME, OP) \
std::vector<Tensor> foreach_tensor_##NAME##_scalar_cuda( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Scalar& scalar) { \
check_foreach_api_restrictions(input, tensors1, tensors2); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalar) || \
has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalar_slow( \
input, tensors1, tensors2, scalar); \
} \
\
return foreach_pointwise_op<OP>(input, tensors1, tensors2, scalar); \
} \
\
void foreach_tensor_##NAME##_scalar_cuda_( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Scalar& scalar) { \
check_foreach_api_restrictions(input, tensors1, tensors2); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalar) || \
has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalar_slow_( \
input, tensors1, tensors2, scalar); \
} \
\
foreach_pointwise_op_<OP>(input, tensors1, tensors2, scalar); \
}
#define FOREACH_POINTWISE_OP_SCALARLIST(NAME, OP) \
std::vector<Tensor> foreach_tensor_##NAME##_scalarlist_cuda( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalars) || \
has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_slow( \
input, tensors1, tensors2, scalars); \
} \
\
return foreach_pointwise_op<OP>(input, tensors1, tensors2, scalars); \
} \
\
void foreach_tensor_##NAME##_scalarlist_cuda_( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalars) || \
has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_slow_( \
input, tensors1, tensors2, scalars); \
} \
\
foreach_pointwise_op_<OP>(input, tensors1, tensors2, scalars); \
}
#define FOREACH_POINTWISE_OP_TENSOR(NAME, OP) \
std::vector<Tensor> foreach_tensor_##NAME##_tensor_cuda( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Tensor& scalars_) { \
auto scalars = convert_tensor_to_scalar_list(scalars_, input.size()); \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
if (!can_use_fast_route({input, tensors1, tensors2}) || \
has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_slow( \
input, tensors1, tensors2, scalars); \
} \
\
return foreach_pointwise_op<OP>(input, tensors1, tensors2, scalars); \
} \
\
void foreach_tensor_##NAME##_tensor_cuda_( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Tensor& scalars_) { \
auto scalars = convert_tensor_to_scalar_list(scalars_, input.size()); \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
if (!can_use_fast_route({input, tensors1, tensors2}, scalars) || \
has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_slow_( \
input, tensors1, tensors2, scalars); \
} \
\
foreach_pointwise_op_<OP>(input, tensors1, tensors2, scalars); \
}
FOREACH_POINTWISE_OP_SCALAR(addcmul, std::multiplies);
FOREACH_POINTWISE_OP_SCALAR(addcdiv, std::divides);
FOREACH_POINTWISE_OP_SCALARLIST(addcmul, std::multiplies);
FOREACH_POINTWISE_OP_SCALARLIST(addcdiv, std::divides);
FOREACH_POINTWISE_OP_TENSOR(addcdiv, std::divides);
FOREACH_POINTWISE_OP_TENSOR(addcmul, std::multiplies);
} // namespace at::native